EP1460933B1 - Systeme et procede d'evaluation des desordres psychologiques au moyen de signaux electroencephalographiques (eeg) - Google Patents

Systeme et procede d'evaluation des desordres psychologiques au moyen de signaux electroencephalographiques (eeg) Download PDF

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EP1460933B1
EP1460933B1 EP03703709A EP03703709A EP1460933B1 EP 1460933 B1 EP1460933 B1 EP 1460933B1 EP 03703709 A EP03703709 A EP 03703709A EP 03703709 A EP03703709 A EP 03703709A EP 1460933 B1 EP1460933 B1 EP 1460933B1
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frequency
indicative
feature
limited
prediction
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EP1460933A2 (fr
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Scott D. Greenwald
Charles P. Smith
Jeffrey C. Sigl
Philip H. Devlin
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Aspect Medical Systems LLC
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Aspect Medical Systems LLC
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A61B5/4839—Diagnosis combined with treatment in closed-loop systems or methods combined with drug delivery
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4088—Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction

Definitions

  • the invention relates to a system and non-therapeutic method for assessing neurological conditions, more particularly to the assessment of dementia, the assessment of depression and the prediction of depression treatment efficacy.
  • the invention may also be applied to the assessment of epilepsy, Parkinson's disease, attention deficit (hyperactive) disorder, stroke, delirium, vigilance and sleep assessment
  • Dementia is a generalized designation for a state of mental deterioration, manifested in cognitive dysfunction, such as memory loss, impaired thinking, and strange behavior.
  • dementia There are many types and causes of dementia, including vascular dementia, Alzheimer's type dementia (ATD), HIV/AIDS-related dementia, alcoholic dementia, depression, Huntington's disease, tumors and Parkinson's disease.
  • ATD is the most common type of dementia and is a progressive, neurological disorder of the brain.
  • ATD is the fourth leading cause of death in adults, after heart disease, cancer, and stroke.
  • ATD Alzheimer's disease
  • Identification of a cause other than Alzheimer's disease also relieves concern about the prognosis.
  • a diagnosis of ATD at an early stage allows the afflicted and their family an opportunity for medical and financial planning.
  • the current treatment methods for ATD offer only short-term symptomatic relief, there are numerous treatments and prevention methods in development that promise a radically improved level of treatment, The widespread application of such therapies will require a much more effective method of diagnosing ATD in its earliest stages, before other symptoms have made their appearance.
  • early diagnosis is important to identify other symptomatically similar disease processes that are often easily treated and possibly reversed.
  • Patients with dementia of many types exhibit changes in EEG in comparison to age-matched normal subjects. Typical changes include increased EEG activity in the delta (0-4 Hz) and theta (4-8 Hz) bands and decreased EEG activity in the beta band (12-30 Hz). This is in contrast to elderly normal subjects, who exhibit decreased low frequency activity and increased high frequency activity with increasing age.
  • EEG power spectra observed at progressively worsening levels of cerebral function, implying a progressive change in EEG parameters that may be used to stage the progression of the dementia.
  • the change in theta power as a percent of the total power has been shown to distinguish between mild, moderate and severe dementia, as well as controls.
  • EEG electroencephalographic signals
  • Depression is a mood disorder that affects 17 million Americans each year, and is responsible for 9.7 million doctor visits. It affects sufferers in a variety of ways, resulting in depressed mood, irritability, sleep disorders, feelings of agitation, guilt and worthlessness, loss of energy and initiative, an inability to concentrate and an increased incidence of suicide. It is difficult to diagnose, due to comorbidities and the fact that it is largely self-reported. There are a number of antidepressant pharmacological agents, and once the proper treatment is determined, their effectiveness is quite high. Selection of the most efficacious agent and the initial dose is largely by trial and error. There is thus a need for an objective measure of depression as well as a method of predicting efficacy of antidepressant treatment.
  • BIS Bispectral Index TM
  • BIS is a univariate processed EEG parameter derived from surface electrodes placed on the forehead and temple.
  • the Bispectral Index is described in U.S. Patent Nos. 4,907,597 ; 5,010,891 ; 5,320,109 and 5,458,117 , all of which are incorporated herein by reference.
  • BIS is a complex parameter, consisting of a set of components that include power spectral and higher order (bispectral) components as well as time domain components. These components are combined into a single number scaled from 0 to 100.
  • BIS has been designed to reflect the hypnotic state of an individual, both while awake and while undergoing anesthesia.
  • the probability of recall is closely related to the hypnotic state.
  • BIS is highly correlated with the probability of both free and cued recall in subjects under the influence of anesthetic and sedative agents.
  • Decreased formation of new memories and an impaired ability to recall preexisting memories are hallmarks of various demential.
  • ATD advanced cognitive dysfunctional dementia
  • BIS was observed to be lower at unmedicated, presurgical baseline in patients with dementia (ATD and multi-infarct dementia) compared to age-matched control subjects.
  • BIS cerebral glucose metabolism is decreased in patients with ATD in comparison to age-matched patients with normal cognitive function.
  • BIS was shown to be correlated with reduction of cerebral glucose metabolism resulting from anesthetic agents, as determined using positron emission tomography imaging. It is thus a reasonable conjecture that the one of the underlying technologies of BIS, bispectral analysis, might be useful in assessing neurological function in a global sense.
  • the present invention is a system and a non-therapeutic method as set out in the appended claims
  • the system and method of the present invention produce at least two features indicative of mood disorder which are indicative of a prediction of the effectiveness of a specific pharmacological treatment.
  • the system and method preferably further incorporate a testing methodology to improve the performance characteristics of the features or indices.
  • power spectrum, time domain, bispectrum and higher order spectrum values are derived from EEG signals taken from the subject being tested.
  • a preferred embodiment of the present invention shown in Figure 1 incorporates a Data Acquisition Unit (DAU) 20 that is used to acquire an EEG signal from a subject 10 for subsequent processing.
  • the DAU 20 typically consists of a computer system with an integral analog-to-digital (A-D) converter 25 and a set of electrodes that is representatively shown placed on the scalp of a subject 10. While only a single electrode 15 is shown, any montage of electrodes used to obtain EEG signals may be used in the invention.
  • the A-D converter 25 is used to transform the analog EEG signals obtained from the electrodes 15 into a sampled set of signal values that may then be analyzed by the processor 35 of a Data Computation Unit (DCU) 30,
  • the DCU 30 incorporates a processor 35 and a communications device that receives the sampled values from the DAU 20.
  • the processors of the DAU 20 and DCU 30 are one and the same.
  • the DAU 20 may acquire the EEG signals and transmit the sampled EEG signals over a communications link to a remote DCU 30.
  • Such a communications link may be a serial or parallel data line, a local or wide area network, a telephone line, the Internet, or a wireless connection.
  • the clinician conducting the assessment may communicate with the DCU 30 using a keyboard 40 and display device 50.
  • an additional keyboard and display device may be attached to the DAU 20 for the use of the clinician.
  • the DCU 30 After the DCU 30 receives the sampled values from the DAU 20, the DCU 30 first examines the sampled EEG signals for artifact arising from patient movement, eye blinks, electrical noise, etc. Detected artifact is either removed from the signal, or the portion of the signal with artifact is excluded from further processing.
  • the EEG signal is also filtered to reduce or remove artifact from high and / or low frequency noise sources, such as electromyographic and radio frequency interference and movement artifact, respectively.
  • High-pass filtering is also employed to reduce the tendency of power at frequencies above the signal band of interest from appearing at lower frequencies due to an inadequate sampling frequency (aliasing).
  • the DCU 30 next computes the set of bispectral arrays from the artifact-free EEG data, as well as additional non-bispectral parameters.
  • Non bispectral parameters may include power spectral arrays, higher-order spectral arrays (trispectrun, etc.), cordance (such as described in US Patent No. 5,269,315 and US Patent No. 5,309,923 ), z-transformed variables, entropy parameters, and time-domain parameters, including but not limited to template matching, peak detection, threshold crossing, zero crossings and Hjorth descriptors.
  • Such parameters, bispectral or otherwise, which quantify some aspect of the data are referred to as features.
  • An index is a function incorporating one or more features as variables.
  • the index function may be linear or nonlinear, or may have an alternative form such as a neural network.
  • the DCU 30 calculates from all the bispectral arrays and non-bispectral parameters a series of features and indices that are indicative of the subject's level of neurological dysfunction, the severity of a neurological condition, or the likelihood of responsiveness to pharmacological treatment. These features and indices may be displayed to the user on the display device 50. In the embodiment in which the DCU 30 is remote from the DAU 20, the result may be transmitted back to the display device on the DAU 20, or transmitted to the patient's physician via e-mail or made available via a secure internet World Wide Web page.
  • the electrodes are preferably of the Zipprep ® type manufactured by Aspect Medical Systems, Inc. (Newton, MA).
  • Zipprep ® type electrodes When electrodes are placed within the hair, gold-cup type electrodes may be used, held in place by either collodion or a physical restraint such as an electrode cap placement device, as provided by various manufacturers. A variety of different electrode placements, or montages, may be used.
  • the bispectral arrays may be calculated using frequency domain (Fourier transform) methods as well as time domain (autoregressive) methods.
  • the term bispectral arrays or bispectrum includes all or any of the following arrays, for both auto and cross formulations: complex triple product, real triple product, bispectral density, biphase and bicoherence arrays.
  • the power spectral arrays are calculated as an intermediate step and are available for the derivation of parameters to be used as features in an index. Both methods will be illustrated here, and those skilled in the art will recognize that other methods may potentially be derived, as well.
  • the invention is intended to incorporate all computational methods.
  • step 802 the system checks whether the computation to be performed is an autobispectral or cross-bispectral computation.
  • Autobispectral analysis is a special case of cross-bispectral analysis and therefore different rules of symmetry apply.
  • step 804 the system sets the following symmetries in order to proceed with autobispectral computation: f 1 + f 2 ⁇ f s / 2 0 ⁇ f 2 ⁇ f 1
  • f s is the sampling rate (128 samples / second in the preferred embodiment which uses 128 2-second records, resulting in a frequency resolution of 0.5 Hz)
  • f 1 and f 2 also referred to as Frequency 1 and Frequency 2 denote the frequency pairs over which bispectral computation will be carried out.
  • X i (f) and Y i (f) denote the Fourier transforms of the time series records X i (t) and Y i (t), respectively, and i denotes the record number.
  • step 806 the following symmetries are adhered to for cross-bispectral analysis: f 1 + f 2 ⁇ f s / 2 0 ⁇ f 1 ⁇ f s / 2 0 ⁇ f 2 ⁇ f s / 2 X i t ⁇ Y i t ⁇ X i f ⁇ Y i f
  • all variables represent the same values as they do for autobispectral analysis, except that for cross-bispectral analysis X i (t) and Y i (t) represent individually derived time series records.
  • the fast Fourier transform (FFT) X i (f) and Y i (f) of the selected records is computed using a standard IEEE library software routine or any other publicly available software routine in step 808.
  • Step 810 the power spectra P Xi (f) and P Yi (f) of each of the selected records is computed by squaring the magnitudes of each element of the Fourier transforms X i (f) and Y i (f), respectively.
  • step 822 the system returns the requested auto/cross bispectral arrays to the Data Computation Unit 30.
  • steps 902, 904, and 906 the system sets the symmetries and time series records in the same manner as described above in steps 802, 804, and 806 respectively.
  • the power spectra of X i (t) and Y i (t) are estimated in steps 908, 910, and 912.
  • This estimation method includes two major stages, the autoregressive (AR) model order selection and the power spectrum computation for X i (t) and Y i (t).
  • step 908 the system computes two sequences of autocorrelations, ⁇ R 2X (m) ⁇ and ⁇ R 2Y (m) ⁇ using the following equation.
  • the autocorrelation sequences, ⁇ R 2X (m) ⁇ and ⁇ R 2Y (m) ⁇ are entered into Levinson recursion with orders Q X and Q Y , respectively, instead of L.
  • 1 + ⁇ i 1 Q z c iz ⁇ e - 2 ⁇ ⁇ fi ⁇
  • 2 z X , Y
  • the system estimates the auto/cross real and complex triple products in steps 914, 916, and 918.
  • the estimation process includes two major stages: the order selection and real and complex triple product computation.
  • step 914 two sequences of third-order moments, ⁇ R 3X ( ⁇ ) and ⁇ R 3Y ( ⁇ ) ⁇ are computed using the following equation.
  • T X and T Y are formed as follows.
  • T z R 3 ⁇ z - L R 3 ⁇ z ⁇ - L + 1 ⁇ R 3 ⁇ z 0
  • R 3 ⁇ z - 1 ⁇ ⁇ ⁇ ⁇ R 3 ⁇ z ⁇ - 2 ⁇ L
  • R 3 ⁇ z ⁇ - 2 ⁇ L + 1 ⁇ R 3 ⁇ z - L z X , Y
  • O X and O Y of the AR filters of the bispectral arrays of X i (t) and Y i (t) are the ranks of the super matrices T X and T Y . Therefore, O X and O Y are chosen by means of singular value decomposition.
  • the system After obtaining the average auto/cross complex and real triple products, the system computes the bispectral density, biphase, and bicoherence arrays in step 920 the same way as in steps 816, 818, 820. In step 922, the system returns the requested bispectral arrays to the Data Computation Unit 30.
  • An index may be constructed using the bispectral arrays and / or other frequency and time domain features. Such an index may be designed to be predictive of the presence or absence of a given disease state.
  • the index may be designed as a classifier, in which an individual to be assessed is predicted as having the disease or not, or in which the probability of having the disease is used as a measure of disease progression.
  • the index may alternately be designed as a continuous predictor of neurological function or disease state / progression. Development of such indices requires a data set consisting of EEG data from individuals with the specified pathological condition, at different levels of progression, as well as control individuals without the specified condition. The data set must also include an independent assessment of the disease state that the index is designed to predict.
  • an index is constructed as a continuous predictor of disease state.
  • EEGs are recorded from elderly normal controls and from patients with either mild to moderate Alzheimer's type dementia (ATD) or multi-infarct dementia (MID). EEG data is recorded when the subjects are in an awake, resting state.
  • ATD Alzheimer's type dementia
  • MID multi-infarct dementia
  • MMSE Mini-Mental State Exam
  • the EEG autobispectral arrays are calculated for all frequency triples of the form (f 1 , f 2 , f 1 +f 2 ) at 1 Hz resolution using 2-sec records of the first 30 seconds of non-drowsy, artifact-free EEG recorded from T3-Fp1 (International 10/20 Electrode Montage System).
  • Statistical assessments are calculated using Spearman rank correlation and Mann-Whitney U non-parametric tests, as appropriate. A statistical significance level of P ⁇ 0.05 is considered statistically significant.
  • the level of dementia as measured by mean MMSE score is statistically different between the control group and each dementia group, but not between dementia groups (Table 1).
  • Table 1 Group Number of Subjects MMSE (mean ⁇ SD) Control 18 29.0 ⁇ 1.1 Alzheimer's Type Dementia 11 16.6 ⁇ 8.6 Multi-infarct Dementia 7 19.4 ⁇ 7.5
  • a diagnostic or monitoring index is often specified to have the form of a linear predictor.
  • a set of features is constructed from the mean value of the regions within the EEG bispectral density array of a single EEG channel that exhibits a strong correlation with MMSE score, as noted above and in Figure 4 .
  • the preferred embodiment uses one channel of EEG data, alternate embodiments may include data from a plurality of channels.
  • the features derived from the bispectral density array that best correlated with MMSE score are the mean values of the regions [0 Hz ⁇ f 1 ⁇ 5 Hz, 0 Hz ⁇ f 2 ⁇ f 1 Hz] and [35 Hz ⁇ f 1 ⁇ 53 Hz, 11 Hz ⁇ f 2 ⁇ upper limit].
  • a 1 and A 2 are the number of frequency pairs in the summation in the calculation of F 1 and F 2 , respectively, and 0 ⁇ j ⁇ i.
  • the correlations of F 1 and F 2 with MMSE are - 0.59 and 0.49, respectively.
  • a third feature F 3 is specified as the ratio of F 1 to F 2 .
  • F 3 F 1 F 3
  • c 3 is defined such that the range of Index Dem_progression will be between 0 and 100, inclusive by using the values min and max, the minima and maxima, respectively.
  • min(F 3 ) 0.9
  • max (F 3 ) 2.6
  • c 0 -52.9
  • c 3 58.8.
  • the correlation of F 3 and thus of Index Dem_progression with MMSE is -0.64, indicating that Index Dem_progression is a sensitive measure of the degree of dementia.
  • an index may be derived to diagnose disease state.
  • the described data set was used to derive an index capable of discriminating patients with diagnosed dementia from normal controls.
  • the features derived from the bispectral density array that best discriminated controls from demented patients are the mean values of the regions [39 Hz ⁇ f 1 ⁇ 41 Hz, 9 Hz ⁇ f 2 ⁇ 11 Hz] and [2 Hz ⁇ f 1 ⁇ 4 Hz, 1 Hz ⁇ f 2 ⁇ 3 Hz].
  • a 4 and A 5 are the number of frequency pairs in the summation in the calculation of F 4 and F 5 .
  • c 0 - 100 ⁇ min F 6 max F 6 - min F 6
  • AUC area under the receiver operating curve
  • the region that best separated ATD from MID patients was the mean value of the region [5 Hz ⁇ f 1 ⁇ 7 Hz, 5 Hz ⁇ f 2 ⁇ 7 Hz].
  • F 7 1
  • BD f 1 i
  • f 2 j
  • a 7 is the number of frequency pairs in the summation in the calculation of F 7 .
  • c 0 - 100 ⁇ min F 7 max F 7 - min F 7
  • c 7 100 max F 7 - min F 7 Index
  • ATD_MID c 0 + c 7 ⁇ F 7
  • the system and method generate an index that predicts the efficacy of drug treatment or measures the state of a disease or condition.
  • indexs that assess the level of depression or use pre-treatment EEG data to predict the response of patients with depression to pharmacological treatment will now be described.
  • the EEG bispectral density arrays were calculated for all frequency pairs (f 1 , f 2 ) at 1 Hz resolution using 2-second records of the first 20-32 seconds of artifact-free EEG recorded from T3-Fp 1 (International 10/20 Electrode Montage System). Statistical assessments were calculated using Spearman rank correlation and Mann-Whitney U non-parametric tests, as appropriate. A statistical significance level of P ⁇ 0.05 was considered statistically significant.
  • the EEG bispectral density array was greater at all frequencies in more deeply depressed patients (i.e., lower Hamilton-D score), particularly in the region [12 Hz ⁇ f 1 ⁇ 24 Hz, 0 ⁇ f 2 ⁇ 6 Hz].
  • indexes may be derived from databases of patients treated with different antidepressant agents, such as fluoxetine. By using such a set of indices, a physician may determine which antidepressant agent is likely to have the greatest treatment response, thus simplifying the trial and error aspect of treating depression. Such a set of indices could also be used to predict the success of treatment with any particular set of antidepressant agents. These indices may be further refined by including in the development database different initial dosages of the various antidepressant agents. This will enable the index or indices to predict not only the most efficacious agent but also the most efficacious initial dose.
  • indices derived from the bispectral density array it is not limited to such indices.
  • Features may be calculated from other regions of the various bispectral arrays (i.e., complex triple product, real triple product, biphase and bicoherence, all for both auto and cross formulations).
  • Other features may also be used to derive features, such as medians, standard deviations and variances, percentiles, absolute power within a region bounded by specified frequencies, relative power (absolute power as a percentage of total power within a region bounded by specified frequencies), neural networks, fractal spectral analysis, measures derived from information theory such as entropy and complexity, and other statistical measures known to those skilled in the art.
  • Features may also be derived from the power spectrum and from various methods of time domain analysis such as pattern matching and fractal analysis.
  • Features may also quantify the presence or absence of a specific condition over time period, or the degree to which a specific condition is met over a specific time period (e.g., the percent of time in a recent period that the power in a specific frequency band of a power or bispectral array was less than a threshold value).
  • Detectors of specific conditions or signal types may also be used as features or as an index having just two states.
  • a further refinement of the system and method of the present invention is to incorporate features derived from the EEG with features derived from analysis of images of the structure under examination (e.g., the brain). Such images may be obtained from CAT (computer-aided tomography), MRI (magnetic resonance imaging), PET (positron emission tomography), X-ray and other modalities. Yet another refinement is to incorporate both features derived from the EEG with features derived from the analysis of images of the function of the structure under analysis. Images of function such as glucose metabolism may be obtained with techniques such as functional PET imaging.
  • Features derived from metrics of the instantaneous or time-averaged glucose metabolism in the entire brain or a specified sub-region of the brain may be combined in an index of CNS function to quantify cognitive function, disease state, disease progression, and other parameters of interest.
  • the sensitivity and specificity of the invention may be increased through the use of differential testing methodologies.
  • Differential test methodologies use 2 or more consecutive assessments, and analyze the change in the value of the test metric as well as the actual values at each of the assessments.
  • the assessments are generally conducted under different conditions, such as sleep or under the influence of a stressor such as a mental task; these are compared to a baseline assessment.
  • Patients with dementia, depression and other neurological disorders exhibit EEG responses different from that of normal subjects in a differential testing methodology.
  • differential testing methodologies may be used to increase the performance of the derived indices.
  • the test metric is an index derived from the EEG bispectral arrays, as well as other non-bispectral parameters, and will be denoted in the description below as INDEX.
  • the first test methodology uses the difference between a first value of INDEX calculated from EEG acquired with the subject's eyes open and a second value of INDEX calculated from EEG acquired with the subject's eyes closed.
  • the electrodes 15 are first applied to the subject 10, who is instructed to sit quietly with eyes open.
  • a segment of EEG is acquired by the DAU 20 and transmitted to the DCU 30 for analysis.
  • segments of several minutes are used to calculate the INDEX values.
  • the subject 10 is next directed to sit quietly with eyes closed, and a second segment of EEG is acquired by the DAU 20 and transmitted to the DCU 30 for analysis.
  • the DCU calculates INDEX values for both the first and second periods of acquired data, referred to as INDEX eyes _ open and INDEX eyes_closed .
  • Examining the acquired data for artifact and either removing the detected artifact or excluding the artifacted portion of the acquired data from analysis is an integral part of calculating an INDEX value.
  • the numerical difference between INDEX eye_open and INDEX eyes_closed is a metric that is indicative of the level of neurological dysfunction, the severity of a condition or a prediction of the efficacy of treatment.
  • a second test methodology uses the difference between a first value of INDEX calculated from EEG acquired with the subject in a relaxed state and a second value of INDEX calculated from EEG acquired while the subject is performing a mental calculation task.
  • the subjects may be directed to keep their eyes open during both recording periods. Alternatively, the subjects may be directed to close their eyes during both recording periods, though this may restrict the mental calculation tasks that may be chosen.
  • the mental calculation task may be any simple task or set of tasks chosen to provide adequate difficulty yet universal enough to not require special training or a level of education not universal in the population to be tested. Two example tasks are mental addition and subtraction of numbers, as would be required in balancing a check book, and the calculation of the number of days between two dates.
  • the electrodes are first applied to the subject, who is instructed to sit quietly with eyes open.
  • a segment of EEG is acquired by the DAU 20 and transmitted to the DCU 30 for analysis. Again, segments of several minutes are used to calculate the INDEX values.
  • the subject is next given instruction in the mental task and then asked to complete it.
  • a second segment of EEG is acquired by the DAU 20 during the period of mental calculation.
  • the acquired data is then transmitted to the DCU 30 for analysis.
  • the DCU 30 calculates INDEX values for both the first and second periods of acquired data, referred to as INDEX baseline and INDEX task .
  • the numerical difference between INDEX baseline and INDEX task is a second metric that is indicative of the level of neurological dysfunction, the severity of a condition or a prediction of the efficacy of treatment.
  • a third test methodology uses the difference between a first value of INDEX calculated from EEG acquired with the subject in an awake, relaxed state and a second value of INDEX calculated from EEG acquired while the subject is sleeping.
  • the electrodes 15 are first applied to the subject 10, who is instructed to sit quietly with eyes either open or closed.
  • a segment of EEG is acquired by the DAU 20 and transmitted to the DCU 30 for analysis. Again, segments of several minutes are used to calculate the INDEX values.
  • the subject then goes to sleep and EEG data is acquired continuously while the subject is sleeping.
  • the second INDEX value is calculated from EEG recorded while the subject is sleeping, preferably in REM sleep.
  • the DCU software implements any of the algorithms known in the art that perform automated identification of REM sleep. These algorithms generally make use of EEG data as well as electrooculograph (EOG) data. Alternately, a trained observer who visually reviews the recorded EEG and enters the starting and ending times of REM sleep into the DAU 20 may identify periods of REM sleep manually. In this case, it is necessary that the DAU 20 have a data entry device such as a computer keyboard.
  • the acquired data is then transmitted to the DCU 30 for analysis.
  • the DCU 30 calculates an INDEX value for the first period of acquired data, which is referred to as INDEX awake .
  • the DCU 30 next calculates an INDEX value from the second period of acquired data, referred to as INDEX sleep .
  • INDEX sleep is preferably calculated during a period of REM sleep
  • the automatic REM sleep identification algorithm must first process the data in order to identify a suitable segment of REM sleep from which to calculate INDEX sleep . If the recorded EEG data is manually reviewed for periods of REM sleep, the starting and ending times transmitted to the DCU 30 are used instead.
  • the numerical difference between INDEX awake and INDEX sleep is a metric that is indicative of the level of neurological dysfunction, the severity of a condition or a prediction of the efficacy of treatment. This type of processing extends to any observation of INDEX changes between or during the awake and asleep states, not simply the comparison with baseline.
  • a further embodiment of the invention utilizes the difference between the value of an INDEX computed from a subject's resting, awake EEG and the value of the INDEX computed from EEG obtained after administration of a hypnotic anesthetic agent.
  • the anesthetic agent is administered manually by means of a syringe.
  • a means of administering such an anesthetic agent is incorporated into the system as shown in Figure 6 , generally a computer-controlled infusion pump 70, or in the case of an inhalational agent, an anesthesia machine designed for administering volatile agents.
  • the infusion pump may be a commercially available type such as a Graseby Model 3400/3500.
  • the infusion pump is controlled by the DAU 20 via a standard RS-232 communication link.
  • the EEG electrodes are first applied to the subject 10. If an infusion pump 70 is used, an intravenous line is placed in the subject's forearm by the clinician who is administering the examination, the pump is loaded with a syringe of the chosen hypnotic anesthetic agent and the intravenous line is connected to the infusion pump.
  • the subject is directed to keep his/her eyes open during both recording periods. Alternately, the subject may be directed to close his/her eyes during both recording periods.
  • a segment of EEG is acquired by the DAU 20 and transmitted to the DCU 30 for analysis. Segments of several minutes are used to calculate the INDEX values.
  • the clinician Upon completion of the baseline recording period, the clinician administers a bolus of hypnotic agent, preferably 0.5 mg/kg of thiopental. If an infusion pump is in use, the DAU 20 instructs the infusion pump to deliver the bolus of anesthetic agent.
  • a second segment of EEG is acquired by the DAU 20 after the bolus of anesthetic agent has reached its maximal effect, generally 3-5 minutes in the preferred embodiment.
  • the acquired data is then transmitted to the DCU 30 for analysis.
  • the DCU 30 calculates INDEX values for both the first and second periods of acquired data, referred to as INDEX baseline and INDEX agent .
  • the numerical difference between the pre-medication baseline value INDEX baseline and the post-medication value INDEX agent is a metric that is indicative of the level of neurological dysfunction, the severity of a condition or a prediction of the efficacy of treatment. For example, subjects with ATD exhibited smaller differences in absolute beta power than non-demented control subjects between baseline and post thiopental administration.
  • the DAU 20 may be equipped with display and data entry devices, such as a computer monitor and keyboard, respectively.
  • the DAU 20 will execute an interface program that will allow communication between the infusion pump and the DAU 20.
  • the DAU 20 may also calculate the volume of infusate, requiring the clinician to enter only the subject's weight and the dilution of the anesthetic agent. If the infusion pump is in use, the clinician may also control the rate of infusion of the anesthetic agent via the interface program. Conversely, the infusion pump may provide information to the DAU 20, confirming its operational status and the administration of the desired bolus of anesthetic agent.
  • the anesthetic agent may be increased in a step-wise regimen consisting of at least two steps, or it may be increased continuously. This is most easily implemented using the infusion pump and interface program previously described and may be preprogrammed into the interface program.
  • This embodiment makes use of a pharmacokinetic (PK) model, which provides a calculated blood plasma concentration from a time series of discrete doses of an anesthetic.
  • the PK model may be integrated with the interface software of the infusion pump, the calculated time series of anesthetic agent doses being communicated to the pump interface software and being used by that software to determine the pump infusion rate.
  • PK software is readily available from several public domain sources; the preferred embodiment uses the RUGLOOP software freely available from Michel Struys, M.D., Department of Anaesthesia, University of Gent, De Pintelaan 185, B-9000 Gent, Belgium. Alternatively, the PK software STANPUMP may be used (freely available from the author, Steven L. Shafer, M.D., Anesthesiology Service (112A), PAVAMC, 3801 Miranda Ave, Palo Alto, CA 94304).

Claims (22)

  1. Système d'évaluation de troubles de l'humeur comprenant une ou plusieurs électrodes pour acquérir des signaux d'électroencéphalogramme (EEG) et un processeur apte à déduire, à partir desdits signaux d'EEG, des valeurs spectrales du 2nd ordre et déduisant à partir desdites valeurs spectrales du 2nd ordre au moins deux caractéristiques indicatives d'un trouble de l'humeur, ladite première caractéristique étant une somme de la puissance totale dans la région des valeurs spectrales du 2nd ordre définie par une plage de fréquences (f1, f2), ladite seconde caractéristique étant une somme de la puissance totale dans la région des valeurs spectrales du 2nd ordre définie par une plage de fréquences (f3, f4), et lesdites au moins deux caractéristiques indicatives d'un trouble de l'humeur étant indicatives d'une prédiction de l'efficacité d'un traitement pharmacologique spécifique.
  2. Système selon la revendication 1, dans lequel ledit processeur est également destiné à combiner lesdites caractéristiques en un indice indicatif de troubles de l'humeur.
  3. Système selon la revendication 1 ou la revendication 2, dans lequel ledit processeur calcule ladite prédiction avant le début dudit traitement pharmacologique.
  4. Système selon la revendication 1, dans lequel ledit état neurologique est une prédiction d'un succès dudit traitement pharmacologique et dans lequel ledit processeur calcule ladite prédiction avant une apparition d'un effet de traitement clinique.
  5. Système selon la revendication 1, dans lequel ledit processeur est destiné à améliorer la précision de test.
  6. Système selon la revendication 5, dans lequel ledit processeur utilise une méthodologie de test différentielle pour améliorer la précision de test.
  7. Système selon la revendication 5, comprenant en outre un dispositif de perfusion relié audit processeur pour administrer une dose contrôlée d'un agent pharmacologique.
  8. Système selon la revendication 1, dans lequel ledit processeur est destiné à déduire, à partir desdits signaux d'EEG, des valeurs de cordance et à déduire à partir des valeurs de cordance au moins une caractéristique indicative de troubles de l'humeur qui est une prédiction de l'efficacité d'un traitement pharmacologique spécifique.
  9. Système selon la revendication 1, dans lequel ladite au moins une caractéristique indicative de troubles de l'humeur est une évaluation du niveau de dépression.
  10. Système selon la revendication 1, dans lequel ladite au moins une caractéristique indicative de troubles de l'humeur est une évaluation de l'efficacité d'un traitement antidépresseur ou est une prédiction du traitement antidépresseur le plus efficace parmi plusieurs traitements.
  11. Système selon la revendication 1,
    dans lequel la première fréquence f1 est limitée suivant 0 Hz < f1 < 10 Hz et la seconde fréquence f2 est limitée suivant 0 Hz < f2 < 10 Hz,
    dans lequel la fréquence f3 est limitée suivant 24 Hz < f3 < 38 Hz et la fréquence f4 est limitée suivant 24 Hz < f4 ≤ fsupérieure, où fsupérieure est la limite supérieure de l'ensemble spectral, de telle sorte que 0 Hz < f4 ≤ f3 et f3 + f4 ≤ 64 Hz, et dans lequel le rapport de ladite première caractéristique à ladite seconde caractéristique est indicatif d'une prédiction de la réponse à un traitement antidépresseur.
  12. Système selon la revendication 1,
    dans lequel au moins l'une desdites caractéristiques est la somme de la puissance totale dans la région des valeurs spectrales du 2nd ordre définie par la plage de fréquences (f1, f2),
    dans lequel la première fréquence f1 est limitée suivant 12 Hz < f1 < 24 Hz et la seconde fréquence f2 est limitée suivant 0 Hz < f2 < 6 Hz, et
    dans lequel ladite caractéristique indicative de troubles de l'humeur est une évaluation du niveau de dépression.
  13. Système selon la revendication 1,
    dans lequel la première fréquence f1 est limitée suivant 0 Hz < f1 ≤ 5 Hz et la seconde fréquence f2 est limitée suivant 0 Hz ≤ f2 ≤ f1,
    dans lequel la fréquence f3 est limitée suivant 35 Hz ≤ f3 ≤ 53 Hz et la fréquence f4 est limitée suivant 11 Hz ≤ f4 ≤ fsupérieure, où fsupérieure est la limite supérieure de l'ensemble spectral, de telle sorte que 0 < f4 ≤ f3 et f3 + f4 ≤ 64 Hz, et
    dans lequel le rapport de ladite première caractéristique à ladite seconde caractéristique est indicative d'une évaluation d'un niveau de démence.
  14. Système selon la revendication 1,
    dans lequel la première fréquence f1 est limitée suivant 39 Hz ≤ f1 ≤ 41 Hz et la seconde fréquence f2 est limitée suivant 9 Hz ≤ f2 ≤ 11 Hz,
    dans lequel la fréquence f3 est limitée suivant 2 Hz ≤ f3 ≤ 4 Hz et la fréquence f4 est limitée suivant 1 Hz ≤ f4 ≤ 3 Hz, et
    dans lequel le rapport de ladite première caractéristique à ladite seconde caractéristique est indicatif d'un diagnostic de démence.
  15. Système selon la revendication 1, dans lequel la première fréquence f1 est limitée suivant 5 Hz ≤ f1 ≤ 7 Hz et la seconde fréquence f2 est limitée suivant 5 Hz ≤ f2 ≤ 7 Hz, et
    dans lequel ladite première caractéristique est indicative d'un diagnostic différentiel d'une démence de type Alzheimer ou d'une démence vasculaire.
  16. Procédé non thérapeutique de prédiction de l'efficacité d'un traitement pharmacologique spécifique comprenant les étapes consistant à :
    acquérir des signaux d'EEG à partir d'un sujet humain,
    déduire desdits signaux d'EEG des valeurs spectrales de 2nd ordre et
    déduire à partir desdites valeurs spectrales de 2nd ordre au moins deux caractéristiques indicatives de troubles de l'humeur, ladite première caractéristique étant une somme de la puissance totale dans la région des valeurs spectrales du 2nd ordre définie par une plage de fréquences (f1, f2), ladite seconde caractéristique étant une somme de la puissance totale dans la région des valeurs spectrales de 2nd ordre définie par une plage de fréquences (f3, f4), et lesdites deux caractéristiques indicatives de troubles de l'humeur étant indicatives d'une prédiction de l'efficacité d'un traitement pharmacologique spécifique.
  17. Procédé selon la revendication 16, comprenant en outre l'étape de combinaison desdites caractéristiques en un indice indicatif de troubles de l'humeur.
  18. Procédé selon la revendication 16, dans lequel ladite au moins une caractéristique indicative de troubles de l'humeur est une prédiction de l'efficacité d'un traitement antidépresseur.
  19. Procédé selon la revendication 16, dans lequel la prédiction est calculée avant le début dudit traitement pharmacologique et/ou avant l'apparition d'un effet de traitement clinique.
  20. Procédé selon la revendication 16, dans lequel lesdits signaux d'EEG sont acquis à partir d'un sujet humain.
  21. Procédé selon la revendication 16, comprenant en outre l'étape d'amélioration de la précision de test, de préférence à l'aide d'une méthodologie de test différentielle.
  22. Procédé selon la revendication 16, comprenant en outre les étapes consistant à :
    déduire à partir desdits signaux d'EEG, des valeurs de cordance, et
    déduire à partir des valeurs de cordance ladite au moins une caractéristique indicative d'une prédiction de l'efficacité d'un traitement pharmacologique spécifique.
EP03703709A 2002-01-04 2003-01-06 Systeme et procede d'evaluation des desordres psychologiques au moyen de signaux electroencephalographiques (eeg) Expired - Lifetime EP1460933B1 (fr)

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WO2003057029A9 (fr) 2004-07-01
DE60325328D1 (de) 2009-01-29
AU2003206411A1 (en) 2003-07-24
MXPA04006544A (es) 2004-10-04
ATE417547T1 (de) 2009-01-15

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